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algorithms for large-scale or distributed training/Robustness, fairness, and personalization in multi-agent learning/Training efficiency and communication reduction/Distributed training of transformer models
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for machine learning, with research topics ranging from decentralized and federated optimization, adaptive stochastic algorithms, and generalization in deep learning, to robustness, privacy, and security
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Your profileYou are enthusiastic about developing and investigating innovative research ideas and systems within the discipline of distributed systems? You have a master’s or equivalent degree in
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focus often neglects other dimensions of processes, such as the time between activities and the time required for specific steps, the spatial distribution of tasks across locations, or the intricate
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, financial networks, e-democracy, voting, social networks, online analysis with delay, and theory of distributed algorithms. In our group, we work on both theory and practice: some members of our group focus
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: Professor Zhongwen Zhan) to design, build, and validate a miniaturized Distributed Acoustic Sensing (DAS) system for the lunar environment. Key responsibilities: - Architect and prototype a low-SWaP (Size
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trigger reconstruction architectures for future particle collider experiments, based on deep learning models distributed across multiple hardware processing stages. The mission of this position, based
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will contribute to areas such as the design and analysis of algorithms (e.g. randomized, quantum, approximation, property testing, online, streaming, sublinear, fine-grained, distributed/parallel) and/or
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the grant and services delivered by ARC longer-term. The ability to work using one or more of these technologies is therefore essential: Compiled languages (e.g. C/Fortran) Shared and distributed memory
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algorithm development to hardware in the loop (HIL) validation, architectures for autonomous coordination through guidance, navigation and control of distributed space segments, under cooperative and non